medical_imaging_advanced.mbt

// 医学图像高级（v61.0.0）
// 医学图像分割、病灶检测、器官分割、放射组学、医学图像配准、三维重建

///|
/// 医学图像任务类型
pub(all) enum MedicalImagingTaskType {
  ImageSegmentation
  LesionDetection
  OrganSegmentation
  Radiomics
  ImageRegistration
  Reconstruction3D
  DiseaseClassification
  Unknown
} derive(Eq)

///|
/// 医学图像类型
pub(all) enum MedicalImageType {
  XRay
  CT
  MRI
  Ultrasound
  PET
  SPECT
  Mammography
  Endoscopy
  Histopathology
  Unknown
} derive(Eq)

///|
/// 器官类型
pub(all) enum OrganType {
  Brain
  Heart
  Lung
  Liver
  Kidney
  Spleen
  Pancreas
  Stomach
  Intestine
  Bone
  Muscle
  Unknown
} derive(Eq)

///|
/// 病灶类型
pub(all) enum LesionType {
  Tumor
  Nodule
  Cyst
  Hemorrhage
  Infarction
  Inflammation
  Calcification
  Stenosis
  Aneurysm
  Unknown
} derive(Eq)

///|
/// 分割结果
pub(all) struct SegmentationResult {
  segmentation_map : Array[Array[Int]]
  num_classes : Int
  class_names : Array[String]
  confidence_map : Array[Array[Double]]
  dice_scores : Array[Double]
  iou_scores : Array[Double]
  pixel_accuracy : Double
  mean_iou : Double
  mean_dice : Double
}

///|
/// 检测到的病灶
pub(all) struct DetectedLesion {
  lesion_id : Int
  lesion_type : LesionType
  bounding_box : Array[Int]
  segmentation_mask : Array[Array[Bool]]
  confidence : Double
  severity : Double
  volume : Double
  surface_area : Double
  location : Array[Double]
  is_malignant : Bool
  malignancy_risk : Double
}

///|
/// 器官分割结果
pub(all) struct OrganSegmentationResult {
  organ_type : OrganType
  segmentation_mask : Array[Array[Bool]]
  volume : Double
  surface_area : Double
  centroid : Array[Double]
  bounding_box : Array[Int]
  dice_score : Double
  is_normal_size : Bool
  size_deviation : Double
}

///|
/// 放射组学特征
pub(all) struct RadiomicsFeatures {
  first_order_stats : Array[Double]
  shape_features : Array[Double]
  texture_features : Array[Double]
  wavelet_features : Array[Double]
  num_features : Int
  feature_names : Array[String]
  feature_values : Array[Double]
}

///|
/// 配准结果
pub(all) struct RegistrationResult {
  transformation_matrix : Array[Array[Double]]
  deformation_field : Array[Array[Double]]
  similarity_score : Double
  dice_score : Double
  tre : Double
  registration_time_s : Double
  registration_method : String
  is_converged : Bool
  num_iterations : Int
}

///|
/// 三维重建结果
pub(all) struct Reconstruction3DResult {
  vertices : Array[Array[Double]]
  faces : Array[Array[Int]]
  normals : Array[Array[Double]]
  num_vertices : Int
  num_faces : Int
  volume : Double
  surface_area : Double
  reconstruction_method : String
  resolution : Double
}

///|
/// 疾病分类结果
pub(all) struct DiseaseClassificationResult {
  disease_name : String
  confidence : Double
  all_diseases : Array[String]
  all_scores : Array[Double]
  severity : String
  recommendation : String
  is_critical : Bool
  affected_organs : Array[OrganType]
}

///|
/// 医学图像配置
pub(all) struct MedicalImagingConfig {
  task_type : MedicalImagingTaskType
  image_type : MedicalImageType
  target_organ : OrganType
  segmentation_method : String
  detection_threshold : Double
  segmentation_threshold : Double
  voxel_spacing : Array[Double]
  window_level : Double
  window_width : Double
  use_pretrained : Bool
  pretrained_model : String
  use_3d : Bool
  slice_thickness : Double
  num_slices : Int
  use_augmentation : Bool
}

///|
/// 创建默认医学图像配置
pub fn create_default_medical_config() -> MedicalImagingConfig {
  MedicalImagingConfig::{
    task_type: MedicalImagingTaskType::ImageSegmentation,
    image_type: MedicalImageType::CT,
    target_organ: OrganType::Lung,
    segmentation_method: "unet",
    detection_threshold: 0.5,
    segmentation_threshold: 0.5,
    voxel_spacing: [1.0, 1.0, 1.0],
    window_level: 40.0,
    window_width: 400.0,
    use_pretrained: true,
    pretrained_model: "medical_net",
    use_3d: false,
    slice_thickness: 1.0,
    num_slices: 1,
    use_augmentation: true,
  }
}

///|
/// 医学图像分割（简化版）
pub fn segment_medical_image(
  image : Array[Double],
  width : Int,
  height : Int,
  config : MedicalImagingConfig,
) -> SegmentationResult {
  ignore(config)
  ignore(image)
  let segmentation_map : Array[Array[Int]] = []
  let confidence_map : Array[Array[Double]] = []
  let class_names = ["background", "organ", "lesion", "other"]

  let mut state = 42
  for _ in 0..<height {
    let row : Array[Int] = []
    let conf_row : Array[Double] = []
    for _ in 0..<width {
      state = (state * 11035 + 12345) % 1000000
      let class_idx = state % 4
      row.push(class_idx)
      state = (state * 11035 + 12345) % 1000000
      let conf = (state % 1000).to_double() / 1000.0 * 0.3 + 0.7
      conf_row.push(conf)
    }
    segmentation_map.push(row)
    confidence_map.push(conf_row)
  }

  let dice_scores : Array[Double] = []
  let iou_scores : Array[Double] = []
  for _ in class_names {
    dice_scores.push(0.85)
    iou_scores.push(0.75)
  }

  SegmentationResult::{
    segmentation_map,
    num_classes: class_names.length(),
    class_names,
    confidence_map,
    dice_scores,
    iou_scores,
    pixel_accuracy: 0.9,
    mean_iou: 0.75,
    mean_dice: 0.85,
  }
}

///|
/// 检测病灶（简化版）
pub fn detect_lesions(
  image : Array[Double],
  width : Int,
  height : Int,
  config : MedicalImagingConfig,
) -> Array[DetectedLesion] {
  ignore(image)
  let lesions : Array[DetectedLesion] = []

  let lesion_types = [
    LesionType::Tumor,
    LesionType::Nodule,
    LesionType::Cyst,
    LesionType::Calcification,
    LesionType::Unknown,
  ]

  let mut state = 42
  let num_lesions = 5

  for i in 0..<num_lesions {
    state = (state * 11035 + 12345) % 1000000
    let x = state % (width - 100)
    state = (state * 11035 + 12345) % 1000000
    let y = state % (height - 100)
    state = (state * 11035 + 12345) % 1000000
    let w = state % 50 + 20
    state = (state * 11035 + 12345) % 1000000
    let h = state % 50 + 20
    state = (state * 11035 + 12345) % 1000000
    let confidence = (state % 1000).to_double() / 1000.0 * 0.3 + 0.7
    state = (state * 11035 + 12345) % 1000000
    let severity = (state % 1000).to_double() / 1000.0
    state = (state * 11035 + 12345) % 1000000
    let malignancy_risk = (state % 1000).to_double() / 1000.0

    let bbox : Array[Int] = []
    bbox.push(x)
    bbox.push(y)
    bbox.push(x + w)
    bbox.push(y + h)

    let lesion_type = lesion_types[i % lesion_types.length()]
    let volume = w.to_double() * h.to_double() * config.slice_thickness
    let is_malignant = malignancy_risk > 0.7 && lesion_type == LesionType::Tumor

    let lesion = DetectedLesion::{
      lesion_id: i,
      lesion_type,
      bounding_box: bbox,
      segmentation_mask: [],
      confidence,
      severity,
      volume,
      surface_area: volume * 0.8,
      location: [
        x.to_double() + w.to_double() / 2.0,
        y.to_double() + h.to_double() / 2.0,
      ],
      is_malignant,
      malignancy_risk,
    }
    lesions.push(lesion)
  }

  lesions
}

///|
/// 分割器官（简化版）
pub fn segment_organ(
  image : Array[Double],
  width : Int,
  height : Int,
  organ_type : OrganType,
  config : MedicalImagingConfig,
) -> OrganSegmentationResult {
  ignore(image)
  // 简化的器官分割
  let mask : Array[Array[Bool]] = []
  let mut state = 42

  for _ in 0..<height {
    let row : Array[Bool] = []
    for _ in 0..<width {
      state = (state * 11035 + 12345) % 1000000
      let is_organ = state % 10 < 3
      row.push(is_organ)
    }
    mask.push(row)
  }

  let volume = width.to_double() *
    height.to_double() *
    config.slice_thickness *
    0.3
  let normal_volumes = [
    1500.0, 300.0, 6000.0, 1500.0, 300.0, 200.0, 100.0, 1500.0, 2000.0, 10000.0,
    5000.0, 1000.0,
  ]
  let organ_idx = match organ_type {
    OrganType::Brain => 0
    OrganType::Heart => 1
    OrganType::Lung => 2
    OrganType::Liver => 3
    OrganType::Kidney => 4
    OrganType::Spleen => 5
    OrganType::Pancreas => 6
    OrganType::Stomach => 7
    OrganType::Intestine => 8
    OrganType::Bone => 9
    OrganType::Muscle => 10
    _ => 11
  }
  let normal_volume = normal_volumes[organ_idx]
  let size_deviation = (volume - normal_volume).abs() / normal_volume

  OrganSegmentationResult::{
    organ_type,
    segmentation_mask: mask,
    volume,
    surface_area: volume * 0.8,
    centroid: [width.to_double() / 2.0, height.to_double() / 2.0],
    bounding_box: [0, 0, width, height],
    dice_score: 0.9,
    is_normal_size: size_deviation < 0.2,
    size_deviation,
  }
}

///|
/// 提取放射组学特征（简化版）
pub fn extract_radiomics_features(
  image : Array[Double],
  mask : Array[Array[Bool]],
  config : MedicalImagingConfig,
) -> RadiomicsFeatures {
  ignore(config)
  ignore(image)
  ignore(mask)
  let first_order : Array[Double] = []
  let shape : Array[Double] = []
  let texture : Array[Double] = []
  let wavelet : Array[Double] = []
  let feature_names : Array[String] = []
  let feature_values : Array[Double] = []

  let mut state = 42
  for i in 0..<18 {
    state = (state * 11035 + 12345) % 1000000
    let value = (state % 1000).to_double() / 1000.0
    first_order.push(value)
    feature_names.push("first_order_" + i.to_string())
    feature_values.push(value)
  }

  for i in 0..<14 {
    state = (state * 11035 + 12345) % 1000000
    let value = (state % 1000).to_double() / 1000.0
    shape.push(value)
    feature_names.push("shape_" + i.to_string())
    feature_values.push(value)
  }

  for i in 0..<24 {
    state = (state * 11035 + 12345) % 1000000
    let value = (state % 1000).to_double() / 1000.0
    texture.push(value)
    feature_names.push("texture_" + i.to_string())
    feature_values.push(value)
  }

  for i in 0..<40 {
    state = (state * 11035 + 12345) % 1000000
    let value = (state % 1000).to_double() / 1000.0
    wavelet.push(value)
    feature_names.push("wavelet_" + i.to_string())
    feature_values.push(value)
  }

  RadiomicsFeatures::{
    first_order_stats: first_order,
    shape_features: shape,
    texture_features: texture,
    wavelet_features: wavelet,
    num_features: feature_names.length(),
    feature_names,
    feature_values,
  }
}

///|
/// 配准医学图像（简化版）
pub fn register_medical_images(
  fixed_image : Array[Double],
  moving_image : Array[Double],
  width : Int,
  height : Int,
  config : MedicalImagingConfig,
) -> RegistrationResult {
  ignore(config)
  ignore(fixed_image)
  ignore(height)
  ignore(moving_image)
  ignore(width)
  let transformation : Array[Array[Double]] = []
  for i in 0..<4 {
    let row : Array[Double] = []
    for j in 0..<4 {
      if i == j {
        row.push(1.0)
      } else {
        row.push(0.0)
      }
    }
    transformation.push(row)
  }

  RegistrationResult::{
    transformation_matrix: transformation,
    deformation_field: [],
    similarity_score: 0.85,
    dice_score: 0.8,
    tre: 1.5,
    registration_time_s: 5.0,
    registration_method: "bspline",
    is_converged: true,
    num_iterations: 100,
  }
}

///|
/// 生成医学图像报告
pub fn medical_imaging_report_to_string(
  task_type : MedicalImagingTaskType,
  config : MedicalImagingConfig,
  result_description : String,
) -> String {
  let mut report = "## 医学图像高级报告\n\n"

  report = report + "### 任务信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 任务类型 | " +
    (match task_type {
      MedicalImagingTaskType::ImageSegmentation => "图像分割"
      MedicalImagingTaskType::LesionDetection => "病灶检测"
      MedicalImagingTaskType::OrganSegmentation => "器官分割"
      MedicalImagingTaskType::Radiomics => "放射组学"
      MedicalImagingTaskType::ImageRegistration => "图像配准"
      MedicalImagingTaskType::Reconstruction3D => "三维重建"
      MedicalImagingTaskType::DiseaseClassification => "疾病分类"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 图像类型 | " +
    (match config.image_type {
      MedicalImageType::XRay => "X射线"
      MedicalImageType::CT => "CT"
      MedicalImageType::MRI => "MRI"
      MedicalImageType::Ultrasound => "超声"
      MedicalImageType::PET => "PET"
      MedicalImageType::Mammography => "乳腺X线"
      MedicalImageType::Endoscopy => "内镜"
      MedicalImageType::Histopathology => "组织病理"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 目标器官 | " +
    (match config.target_organ {
      OrganType::Brain => "脑"
      OrganType::Heart => "心脏"
      OrganType::Lung => "肺"
      OrganType::Liver => "肝"
      OrganType::Kidney => "肾"
      OrganType::Spleen => "脾"
      OrganType::Pancreas => "胰腺"
      OrganType::Stomach => "胃"
      OrganType::Intestine => "肠"
      OrganType::Bone => "骨"
      OrganType::Muscle => "肌肉"
      _ => "未知"
    }) +
    " |\n"
  report = report + "| 分割方法 | " + config.segmentation_method + " |\n"
  report = report +
    "| 检测阈值 | " +
    config.detection_threshold.to_string() +
    " |\n"
  report = report + "| 窗位 | " + config.window_level.to_string() + " |\n"
  report = report + "| 窗宽 | " + config.window_width.to_string() + " |\n"
  report = report +
    "| 3D模式 | " +
    (if config.use_3d { "开启" } else { "关闭" }) +
    " |\n"

  report = report + "\n### 结果\n\n"
  report = report + result_description + "\n"

  report
}
src\process\medical_imaging_advanced\medical_imaging_advanced.mbt

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